Adv. Artif. Intell. Mach. Learn.最新文献

FishRecGAN: An End to End GAN Based Network for Fisheye Rectification and Calibration FishRecGAN:一种基于端到端GAN的鱼眼校正和校准网络

Adv. Artif. Intell. Mach. Learn. Pub Date : 2023-05-09 DOI: 10.48550/arXiv.2305.05222

Xin Shen, Kyungdon Joo, Jean Oh

引用次数: 8

Should ChatGPT and Bard Share Revenue with Their Data Providers? A New Business Model for the AI Era ChatGPT和Bard是否应该与其数据提供商分享收入?人工智能时代的新商业模式

Adv. Artif. Intell. Mach. Learn. Pub Date : 2023-05-04 DOI: 10.48550/arXiv.2305.02555

Dong Zhang

{"title":"Should ChatGPT and Bard Share Revenue with Their Data Providers? A New Business Model for the AI Era","authors":"Dong Zhang","doi":"10.48550/arXiv.2305.02555","DOIUrl":"https://doi.org/10.48550/arXiv.2305.02555","url":null,"abstract":"With various AI tools such as ChatGPT becoming increasingly convenient and popular, we are entering a true AI era. We can foresee that exceptional AI tools will soon reap considerable profits. A crucial question arise: should AI tools share revenue with their training data providers in additional to traditional stakeholders and shareholders? The answer is Yes. Large AI tools, such as large language models, always require more and better quality data to continuously improve, but current copyright laws limit their access to various types of data. Sharing revenue between AI tools and their data providers could transform the current hostile zero-sum game relationship between AI tools and a majority of copyrighted data owners into a collaborative and mutually beneficial one, which is necessary to facilitate the development of a virtuous cycle among AI tools, their users and data providers that drives forward AI technology and builds a healthy AI ecosystem. However, current revenue-sharing business models do not work for AI tools in the forthcoming AI era, since the most widely used metrics for website-based traffic and action, such as clicks, will be replaced by new metrics such as prompts and cost per prompt for generative AI tools. Therefore, a completely new revenue-sharing business model must be established. This new business model, which must be independent of AI tools and be easily explained to data providers, needs to establish a prompt-based scoring system to measure data engagement of each data provider. This paper systematically discusses how to build such a scoring system for all data providers for AI tools based on classification and content similarity models, and outlines the requirements for AI tools or third parties to build it. AI tools can share revenue with data providers using such a scoring system, which would encourage more data owners to participate in the revenuesharing program. This will be a utilitarian AI era where all parties benefit.","PeriodicalId":373878,"journal":{"name":"Adv. Artif. Intell. Mach. Learn.","volume":"89 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120879073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Structural Vibration Signal Denoising Using Stacking Ensemble of Hybrid CNN-RNN 基于混合CNN-RNN叠加综的结构振动信号去噪

Adv. Artif. Intell. Mach. Learn. Pub Date : 2023-03-11 DOI: 10.54364/AAIML.2023.1165

Youzhi Liang, Wen-Chieh Liang, Jianguo Jia

{"title":"Structural Vibration Signal Denoising Using Stacking Ensemble of Hybrid CNN-RNN","authors":"Youzhi Liang, Wen-Chieh Liang, Jianguo Jia","doi":"10.54364/AAIML.2023.1165","DOIUrl":"https://doi.org/10.54364/AAIML.2023.1165","url":null,"abstract":"Vibration signals have been increasingly utilized in various engineering fields for analysis and monitoring purposes, including structural health monitoring, fault diagnosis and damage detection, where vibration signals can provide valuable information about the condition and integrity of structures. In recent years, there has been a growing trend towards the use of vibration signals in the field of bioengineering. Activity-induced structural vibrations, particularly footstep-induced signals, are useful for analyzing the movement of biological systems such as the human body and animals, providing valuable information regarding an individual’s gait, body mass, and posture, making them an attractive tool for health monitoring, security, and human-computer interaction. However, the presence of various types of noise can compromise the accuracy of footstep-induced signal analysis. In this paper, we propose a novel ensemble model that leverages both the ensemble of multiple signals and of recurrent and convolutional neural network predictions. The proposed model consists of three stages: preprocessing, hybrid modeling, and ensemble. In the preprocessing stage, features are extracted using the Fast Fourier Transform and wavelet transform to capture the underlying physics-governed dynamics of the system and extract spatial and temporal features. In the hybrid modeling stage, a bi-directional LSTM is used to denoise the noisy signal concatenated with FFT results, and a CNN is used to obtain a condensed feature representation of the signal. In the ensemble stage, three layers of a fully-connected neural network are used to produce the final denoised signal. The proposed model addresses the challenges associated with structural vibration signals, which outperforms the prevailing algorithms for a wide range of noise levels, evaluated using PSNR, SNR, and WMAPE.","PeriodicalId":373878,"journal":{"name":"Adv. Artif. Intell. Mach. Learn.","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129122992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A Comparison of Methods for Neural Network Aggregation 神经网络聚合方法的比较

Adv. Artif. Intell. Mach. Learn. Pub Date : 2023-03-06 DOI: 10.48550/arXiv.2303.03488

John Pomerat, Aviv Segev

引用次数: 0

One-class Damage Detector Using Deeper Fully Convolutional Data Descriptions for Civil Application 基于深度全卷积数据描述的一类民用损伤检测器

Adv. Artif. Intell. Mach. Learn. Pub Date : 2023-03-03 DOI: 10.54364/aaiml.2023.1159

Takato Yasuno, M. Okano, Junichiro Fujii

{"title":"One-class Damage Detector Using Deeper Fully Convolutional Data Descriptions for Civil Application","authors":"Takato Yasuno, M. Okano, Junichiro Fujii","doi":"10.54364/aaiml.2023.1159","DOIUrl":"https://doi.org/10.54364/aaiml.2023.1159","url":null,"abstract":"Infrastructure managers must maintain high standards to ensure user satisfaction during the lifecycle of infrastructures. Surveillance cameras and visual inspections have enabled progress in automating the detection of anomalous features and assessing the occurrence of deterioration. However, collecting damage data is typically time consuming and requires repeated inspections. The one-class damage detection approach has an advantage in that normal images can be used to optimize model parameters. Additionally, visual evaluation of heatmaps enables us to understand localized anomalous features. The authors highlight damage vision applications utilized in the robust property and localized damage explainability. First, we propose a civil-purpose application for automating one-class damage detection reproducing a Fully Convolutional Data Description (FCDD) as a baseline model. We have obtained accurate and explainable results demonstrating experimental studies on concrete damage and steel corrosion in civil engineering. Additionally, to develop a more robust application, we applied our method to another outdoor domain that contains complex and noisy backgrounds using natural disaster datasets collected using various devices. Furthermore, we propose a valuable solution of deeper FCDDs focusing on other powerful backbones to improve the performance of damage detection and implement ablation studies on disaster datasets. The key results indicate that the deeper FCDDs outperformed the baseline FCDD on datasets representing natural disaster damage caused by hurricanes, typhoons, earthquakes, and fourevent disasters.","PeriodicalId":373878,"journal":{"name":"Adv. Artif. Intell. Mach. Learn.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133334335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Evolutionary Augmentation Policy Optimization for Self-supervised Learning 自监督学习的进化增强策略优化

Adv. Artif. Intell. Mach. Learn. Pub Date : 2023-03-02 DOI: 10.48550/arXiv.2303.01584

Noah Barrett, Zahra Sadeghi, S. Matwin

{"title":"Evolutionary Augmentation Policy Optimization for Self-supervised Learning","authors":"Noah Barrett, Zahra Sadeghi, S. Matwin","doi":"10.48550/arXiv.2303.01584","DOIUrl":"https://doi.org/10.48550/arXiv.2303.01584","url":null,"abstract":"Self-supervised Learning (SSL) is a machine learning algorithm for pretraining Deep Neural Networks (DNNs) without requiring manually labeled data. The central idea of this learning technique is based on an auxiliary stage aka pretext task in which labeled data are created automatically through data augmentation and exploited for pretraining the DNN. However, the effect of each pretext task is not well studied or compared in the literature. In this paper, we study the contribution of augmentation operators on the performance of self supervised learning algorithms in a constrained settings. We propose an evolutionary search method for optimization of data augmentation pipeline in pretext tasks and measure the impact of augmentation operators in several SOTA SSL algorithms. By encoding different combination of augmentation operators in chromosomes we seek the optimal augmentation policies through an evolutionary optimization mechanism. We further introduce methods for analyzing and explaining the performance of optimized SSL algorithms. Our results indicate that our proposed method can find solutions that outperform the accuracy of classification of SSL algorithms which confirms the influence of augmentation policy choice on the overall performance of SSL algorithms. We also compare optimal SSL solutions found by our evolutionary search mechanism and show the effect of batch size in the pretext task on two visual datasets.","PeriodicalId":373878,"journal":{"name":"Adv. Artif. Intell. Mach. Learn.","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115877815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EuclidNet: Deep Visual Reasoning for Constructible Problems in Geometry 欧几里得网:几何中可构造问题的深度视觉推理

Adv. Artif. Intell. Mach. Learn. Pub Date : 2022-12-27 DOI: 10.54364/aaiml.2023.1152

M. Wong, Xintong Qi, C. Tan

引用次数: 1

Predicting Survival of Tongue Cancer Patients by Machine Learning Models 用机器学习模型预测舌癌患者的生存

Adv. Artif. Intell. Mach. Learn. Pub Date : 2022-12-23 DOI: 10.48550/arXiv.2212.12114

Angelos Vasilopoulos, N. Xi

引用次数: 0

Prediction of Auto Insurance Risk Based on t-SNE Dimensionality Reduction 基于t-SNE降维的车险风险预测

Adv. Artif. Intell. Mach. Learn. Pub Date : 2022-12-19 DOI: 10.54364/AAIML.2022.1139

J. Levitas, Konstantin Yavilberg, O. Korol, Genadi Man

引用次数: 0

Can a face tell us anything about an NBA prospect? - A Deep Learning approach 一张脸能告诉我们NBA的前景吗?-深度学习方法

Adv. Artif. Intell. Mach. Learn. Pub Date : 2022-12-13 DOI: 10.48550/arXiv.2212.06804

A. Gavros, Foteini Gavrou

引用次数: 0